Structural intervention at one bridge decreases the overall jumping suicide rate in Victoria, Australia.

AIMS: There is clear evidence that installing safety barriers is effective in preventing jumping suicides from high-risk bridges with only moderate displacement to other nearby bridges. However, the impact of barriers on jumping suicides across broader geographical areas is not well understood. We examined patterns in jumping suicides across the state of Victoria, Australia, after a safety barrier was installed at the West Gate Bridge which, before the installation of the barrier, was the site of approximately 40% of Victoria's jumping suicides. METHODS: We used negative binomial regression analyses on Victorian data from 2000 to 2019 to compare rates of jumping suicides at the West Gate Bridge, other bridges and non-bridge jumping locations before, during and after the West Gate Bridge barrier installation. We conducted linear regression analyses to examine whether the distance travelled from the deceased's usual residence to the location of their jumping suicide changed between the before, during and after barrier installation periods. RESULTS: After installation of the barrier, there were no jumping suicides at the West Gate Bridge (rate ratio [RR] = 0.00, 95% credible intervals [95% Cr] = 0.00-0.0001) and there was strong evidence that the rate of jumping suicides at all locations declined by 65% (RR = 0.35, 95% Cr = 0.22-0.54). At other bridges, there was also evidence of a reduction (RR = 0.31, 95% Cr = 0.11-0.70), but there was no evidence of a change at non-bridge locations (RR = 0.74, 95% Cr = 0.39-1.30). CONCLUSION: After installation of the safety barrier at the West Gate Bridge, jumping suicide in Victoria decreased overall and at other bridges, and did not appear to change at non-bridge locations. Our findings show that when barriers are installed at a site responsible for a disproportionately high number of jumping suicides, they are not only highly effective at the site where the barriers are installed but can also have a prevention impact beyond the immediate locale at similar sites.

Insights for Conducting Large-Scale Surveys with Veterans Who Have Experienced Homelessness.

Surveys of underserved patient populations are needed to guide quality improvement efforts but are challenging to implement. The goal of this study was to describe recruitment and response to a national survey of Veterans with homeless experience (VHE). We randomly selected 14,340 potential participants from 26 U.S. Department of Veterans Affairs (VA) facilities. A survey contract organization verified/updated addresses from VA administrative data with a commercial address database, then attempted to recruit VHE through 4 mailings, telephone follow-up, and a $10 incentive. We used mixed-effects logistic regressions to test for differences in survey response by patient characteristics. The response rate was 40.2% (n=5,766). Addresses from VA data elicited a higher response rate than addresses from commercial sources (46.9% vs 31.2%, p<.001). Residential addresses elicited a higher response rate than business addresses (43.8% vs 26.2%, p<.001). Compared to non-respondents, respondents were older, less likely to have mental health, drug, or alcohol conditions, and had fewer VA housing and emergency service visits. Collectively, our results indicated a national mailed survey approach is feasible and successful for reaching VA patients who have recently experienced homelessness. These findings offer insight into how health systems can obtain perspectives of socially disadvantaged groups.

The Bay Area Muslim mental health community advisory board: evaluation of a community based participatory approach.

AIMS: The aim of this paper is to present a novel case for the formation, operation and evaluation of a community advisory aboard comprised of Muslims residing in the San Francisco Bay Area, California that utilised a community based participatory approach to address local Muslim mental health needs. The CAB was recruited in partnership with the Muslim Community Association (MCA), one of the largest Islamic centres in the San Franscisco Bay Area. In addition to describing the development of the CAB, the authors present the findings of the evaluation and synthesis of best processes based on CAB members' feedback. METHODS: To evaluate the perceived community advisory board members' perceptions of their roles and elicit feedback on how to enhance the relationship between the university team and the CAB, an evaluation was conducted by an independent team who was not part of the research process. Data was collected using anonymous individual surveys and small group open discussions that were conducted over three evaluation meetings. The evaluation utilised mixed method data collection strategies using questions from Schulz et al. (, Evaluation and Program Planning 26, 249-262), an instrument for evaluating dimensions of group dynamics within CBPR partnerships. RESULTS: Results of the evaluation within the sphere of CAB operation indicated that CAB members found the greatest satisfaction from their contributions through direct participation in the research activities that were conducted by the university-CAB team. The collective responses indicated that most CAB members were satisfied with trust built between the university-CAB team and the diversity represented in the members of the board. However, given that the Bay Area is home to a very diverse Muslim community, challenges in recruiting representatives that account for all possible self-identifying groups was reported by the CAB with recommendations to recruit religious leaders. Recommendations also included eliciting funds for potential financial compensation for CAB members. CONCLUSIONS: The Stanford-San Francisco Bay Area CAB demonstrated that empowering community members through direct participation, creating channels and safe spaces for feedback help create community rooted research that carry the true voices of marginalised communities and reflects their evolving needs.

Considerations for supporting meaningful stakeholder engagement in global mental health research.

The need to ensure that research evidence is adopted by health systems and is informed by lived experience expertise has been increasingly recognised in mental health research. In the field of global mental health (GMH), though some progress has been made, the meaningful engagement of key stakeholders in research remains low. This editorial outlines recommendations to support the meaningful engagement of policy makers and people with lived or living experience of mental illness in GMH research. Recommendations include: increasing funding structures that are designed to support meaningful engagement; urging institutions to consider administrative structures that support engagement with lower resourced partners; promoting capacity development opportunities and resources to support researchers to promote meaningful engagement; developing research governance structures that include key stakeholders; and, taking steps to ensure the needs of diverse stakeholders are met through their engagement in research. Examples of good practice from these areas are provided. Though not an exhaustive list of recommendations, this editorial represents a call to the GMH research community to take a deliberate and proactive approach to prioritising meaningful stakeholder engagement in GMH research with the ultimate goal of improving accessible and appropriate mental health care.

Innovative Directions to Advance Mental Health Disparities Research.

Disparities in mental health have persisted or worsened despite our awareness of their existence, increased understanding of their causes, and efforts at reduction and mitigation. Although much is known, there is still much to be done in mental health research to meaningfully impact disparities. In November 2020, the National Institute of Mental Health (NIMH) and the National Institute of Minority Health and Health Disparities (NIMHD) co-sponsored a virtual workshop to explore the complexities of mental health disparities, which revealed several gaps and opportunities for the field to pursue to advance mental health disparities research. This article, the introduction to a Special Issue on Mental Health Disparities, provides a frame for four articles that stem from and are inspired by the virtual NIMH/NIMHD workshop, all of which illustrate innovative research on understanding the complex mechanisms of disparities and how this knowledge can be translated into effective intervention development that advances mental health equity.

Lack of Representation in Psychiatric Research: A Data-Driven Example From Scientific Articles Published in 2019 and 2020 in the American Journal of Psychiatry.

OBJECTIVE: The authors examined representation and accuracy of descriptions of sociodemographic identities in psychiatric research through quantifying data contained in recently published articles from a high-impact psychiatry journal. METHODS: Sociodemographic data were aggregated from articles (i.e., studies that provide information on individual samples) published in the American Journal of Psychiatry in 2019 and 2020 (N=125). Articles were coded by two raters for sociodemographic data, acknowledgment of lack of representation as a limitation, and focus on health disparities or inequities. RESULTS: While 90% of studies provided the age of participants and 84% provided information about the sex/gender of participants, only 43% presented information about the racial or ethnicity identities of participants. One study reported the sexual identity of participants. Lack of representation relative to 2019 U.S. Census data was found for multiple racial groups, Latino/Hispanic individuals, and women (genetic studies only). Only 25% of studies acknowledged lack of representation as a limitation, and two studies focused on health disparities or inequities. CONCLUSIONS: These findings highlight a need to increase representation in psychiatric research and improve accuracy of language when describing the sociodemographic characteristics of participants.

Uncovering survivorship bias in longitudinal mental health surveys during the COVID-19 pandemic.

AIMS: Markedly elevated adverse mental health symptoms were widely observed early in the coronavirus disease-2019 (COVID-19) pandemic. Unlike the U.S., where cross-sectional data indicate anxiety and depression symptoms have remained elevated, such symptoms reportedly declined in the U.K., according to analysis of repeated measures from a large-scale longitudinal study. However, nearly 40% of U.K. respondents (those who did not complete multiple follow-up surveys) were excluded from analysis, suggesting that survivorship bias might partially explain this discrepancy. We therefore sought to assess survivorship bias among participants in our longitudinal survey study as part of The COVID-19 Outbreak Public Evaluation (COPE) Initiative. METHODS: Survivorship bias was assessed in 4039 U.S. respondents who completed surveys including the assessment of mental health as part of The COPE Initiative in April 2020 and were invited to complete follow-up surveys. Participants completed validated screening instruments for symptoms of anxiety, depression and insomnia. Survivorship bias was assessed for (1) demographic differences in follow-up survey participation, (2) differences in initial adverse mental health symptom prevalence adjusted for demographic factors and (3) differences in follow-up survey participation based on mental health experiences adjusted for demographic factors. RESULTS: Adjusting for demographics, individuals who completed only one or two out of four surveys had significantly higher prevalence of anxiety and depression symptoms in April 2020 (e.g. one-survey v. four-survey, anxiety symptoms, adjusted prevalence ratio [aPR]: 1.30, 95% confidence interval [CI]: 1.08-1.55, p = 0.0045; depression symptoms, aPR: 1.43, 95% CI: 1.17-1.75, p = 0.00052). Moreover, individuals who experienced incident anxiety or depression symptoms had significantly higher adjusted odds of not completing follow-up surveys (adjusted odds ratio [aOR]: 1.68, 95% CI: 1.22-2.31, p = 0.0015, aOR: 1.56, 95% CI: 1.15-2.12, p = 0.0046, respectively). CONCLUSIONS: Our findings reveal significant survivorship bias among longitudinal survey respondents, indicating that restricting analytic samples to only respondents who provide repeated assessments in longitudinal survey studies could lead to overly optimistic interpretations of mental health trends over time. Cross-sectional or planned missing data designs may provide more accurate estimates of population-level adverse mental health symptom prevalence than longitudinal surveys.

Uncovering Survivorship Bias in Longitudinal Mental Health Surveys During the COVID-19 Pandemic.

AIMS: Markedly elevated adverse mental health symptoms were widely observed early in the coronavirus disease 2019 (COVID-19) pandemic. Unlike the U.S., where cross-sectional data indicate anxiety and depression symptoms have remained elevated, such symptoms reportedly declined in the U.K., according to analysis of repeated measures from a largescale longitudinal study. However, nearly 40% of U.K. respondents (those who did not complete multiple follow-up surveys) were excluded from analysis, suggesting that survivorship bias might partially explain this discrepancy. We therefore sought to assess survivorship bias among participants in our longitudinal survey study as part of The COVID-19 Outbreak Public Evaluation (COPE) Initiative. METHODS: Survivorship bias was assessed 4,039 U.S. respondents who completed surveys including the assessment of mental health as part of The COPE Initiative in April 2020 and were invited to complete follow-up surveys. Participants completed validated screening instruments for symptoms of anxiety, depression, and insomnia. Survivorship bias was assessed for (1) demographic differences in follow-up survey participation, (2) differences in initial adverse mental health symptom prevalences adjusted for demographic factors, and (3) differences in follow-up survey participation based on mental health experiences adjusted for demographic factors. RESULTS: Adjusting for demographics, individuals who completed only one or two out of four surveys had higher prevalences of anxiety and depression symptoms in April 2020 (e.g., one-survey versus four-survey, anxiety symptoms, adjusted prevalence ratio [aPR]: 1.30, 95% confidence interval [CI]: 1.08-1.55, P=0.0045; depression symptoms, aPR: 1.43, 95% CI: 1.17-1.75, P=0.00052). Moreover, individuals who experienced incident anxiety or depression symptoms had higher odds of not completing follow-up surveys (adjusted odds ratio [aOR]: 1.68, 95% CI: 1.22-2.31, P=0.0015, aOR: 1.56, 95% CI: 1.15-2.12, P=0.0046, respectively). CONCLUSIONS: Our findings revealed significant survivorship bias among longitudinal survey respondents, indicating that restricting analytic samples to only respondents who provide repeated assessments in longitudinal survey studies could lead to overly optimistic interpretations of mental health trends over time. Cross-sectional or planned missing data designs may provide more accurate estimates of population-level adverse mental health symptom prevalences than longitudinal surveys.

A Bayesian approach to estimating the population prevalence of mood and anxiety disorders using multiple measures.

AIMS: There is currently no universally accepted measure for population-based surveillance of mood and anxiety disorders. As such, the use of multiple linked measures could provide a more accurate estimate of population prevalence. Our primary objective was to apply Bayesian methods to two commonly employed population measures of mood and anxiety disorders to make inferences regarding the population prevalence and measurement properties of a combined measure. METHODS: We used data from the 2012 Canadian Community Health Survey - Mental Health linked to health administrative databases in Ontario, Canada. Structured interview diagnoses were obtained from the survey, and health administrative diagnoses were identified using a standardised algorithm. These two prevalence estimates, in addition to data on the concordance between these measures and prior estimates of their psychometric properties, were used to inform our combined estimate. The marginal posterior densities of all parameters were estimated using Hamiltonian Monte Carlo (HMC), a Markov Chain Monte Carlo technique. Summaries of posterior distributions, including the means and 95% equally tailed posterior credible intervals, were used for interpretation of the results. RESULTS: The combined prevalence mean was 8.6%, with a credible interval of 6.8-10.6%. This combined estimate sits between Bayesian-derived prevalence estimates from administrative data-derived diagnoses (mean = 7.4%) and the survey-derived diagnoses (mean = 13.9%). The results of our sensitivity analysis suggest that varying the specificity of the survey-derived measure has an appreciable impact on the combined posterior prevalence estimate. Our combined posterior prevalence estimate remained stable when varying other prior information. We detected no problematic HMC behaviour, and our posterior predictive checks suggest that our model can reliably recreate our data. CONCLUSIONS: Accurate population-based estimates of disease are the cornerstone of health service planning and resource allocation. As a greater number of linked population data sources become available, so too does the opportunity for researchers to fully capitalise on the data. The true population prevalence of mood and anxiety disorders may reside between estimates obtained from survey data and health administrative data. We have demonstrated how the use of Bayesian approaches may provide a more informed and accurate estimate of mood and anxiety disorders in the population. This work provides a blueprint for future population-based estimates of disease using linked health data.

Preserving equipoise and performing randomised trials for COVID-19 social distancing interventions.

In the coronavirus disease 2019 (COVID-19) pandemic, a large number of non-pharmaceutical measures that pertain to the wider group of social distancing interventions (e.g. public gathering bans, closures of schools, workplaces and all but essential business, mandatory stay-at-home policies, travel restrictions, border closures and others) have been deployed. Their urgent deployment was defended with modelling and observational data of spurious credibility. There is major debate on whether these measures are effective and there is also uncertainty about the magnitude of the harms that these measures might induce. Given that there is equipoise for how, when and if specific social distancing interventions for COVID-19 should be applied and removed/modified during reopening, we argue that informative randomised-controlled trials are needed. Only a few such randomised trials have already been conducted, but the ones done to-date demonstrate that a randomised trials agenda is feasible. We discuss here issues of study design choice, selection of comparators (intervention and controls), choice of outcomes and additional considerations for the conduct of such trials. We also discuss and refute common counter-arguments against the conduct of such trials.

Understanding the characteristics and mechanisms underlying suicide clusters in Australian youth: a comparison of cluster detection methods.

AIMS: There is currently no gold-standard definition or method for identifying suicide clusters, resulting in considerable heterogeneity in the types of suicide clusters that are detected. This study sought to identify the characteristics, mechanisms and parameters of suicide clusters using three cluster detection methods. Specifically, the study aimed to: (1) determine the overlap in suicide clusters among each method, (2) compare the spatial and temporal parameters associated with different suicide clusters and (3) identify the demographic characteristics and rates of exposure to suicide among cluster and non-cluster members. METHODS: Suicide data were obtained from the National Coronial Information System. N = 3027 Australians, aged 10-24 who died by suicide in 2006-2015 were included. Suicide clusters were determined using: (1) poisson scan statistics, (2) a systematic search of coronial inquests and (3) descriptive network analysis. These methods were chosen to operationalise three different definitions of suicide clusters, namely clusters that are: (1) statistically significant, (2) perceived to be significant and (3) characterised by social links among three or more suicide descendants. For each method, the demographic characteristics and rates of exposure to suicide were identified, in addition to the maximum duration of suicide clusters, the geospatial overlap between suicide clusters, and the overlap of individual cluster members. RESULTS: Eight suicide clusters (69 suicides) were identified from the scan statistic, seven (40 suicides) from coronial inquests; and 11 (37 suicides) from the descriptive network analysis. Of the eight clusters detected using the scan statistic, two overlapped with clusters detected using the descriptive network analysis and one with clusters identified from coronial inquests. Of the seven clusters from coronial inquests, four overlapped with clusters from the descriptive network analysis and one with clusters from the scan statistic. Overall, 9.2% (12 suicides) of individuals were identified by more than one method. Prior exposure to suicide was 10.1% (N = 7) in clusters from the scan statistic, 32.5% (N = 13) in clusters from coronial inquest and 56.8% (N = 21) in clusters from the descriptive network analysis. CONCLUSION: Each method identified markedly different suicide clusters. Evidence of social links between cluster members typically involved clusters detected using the descriptive network analysis. However, these data were limited to the availability information collected as part of the police and coroner investigation. Communities tasked with detecting and responding to suicide clusters may benefit from using the spatial and temporal parameters revealed in descriptive studies to inform analyses of suicide clusters using inferential methods.

Poor implementation of the EU clinical trial regulation is a major threat for pragmatic trials in European countries.

The principle of pragmatism in clinical trials has been broadly recognised as a way to close the gap between research and practice. In this contribution, we argue that the conduct of pragmatic clinical trials in Europe may be hampered by poor implementation of current European Union's Clinical Trial Regulation No. 536/2014.

Comparative clinical trials in psychotherapy: Have large effects been replicated?

AIMS: The purpose of this review is to examine the replication attempts of psychotherapy clinical trials for depression and anxiety. We focus specifically on replications of trials that exhibit large differences between psychotherapies. The replicability of these trials is especially important for meta-analysis, where the inclusion of false-positive trials can lead to erroneous conclusions about treatment efficacy. METHODS: Standard replication criteria were developed to distinguish direct from conceptual replication methodologies. Next, an exhaustive literature search was conducted for published meta-analyses of psychotherapy comparisons. Trials that exhibited large effects (d > 0.8) were culled from these meta-analyses. For each trial, a cited replication was conducted to determine if the trial had been subsequently replicated by either 'direct' or 'conceptual' methods. Finally, a broader search was conducted to examine the extent of replication efforts in the psychotherapy literature overall. RESULTS: In the meta-analytic search, a total of N = 10 meta-analyses met the inclusion criteria. From these meta-analyses, N = 12 distinct trials exhibited large effect sizes. The meta-analyses containing more than two large effect trials reported evidence for treatment superiority. A cited replication search yielded no direct replication attempts (N = 0) for the trials with large effects, and N = 4 conceptual replication attempts of average or above average quality. However, of these four attempts, only two partially corroborated the results from their original trial. CONCLUSION: Meta-analytic reviews are influenced by trials with large effects, and it is not uncommon for these reviews to contain several such trials. Since we find no evidence that trials with such large effects are directly replicable, treatment superiority conclusions from these reviews are highly questionable. To enhance the quality of clinical science, the development of authoritative replication criteria for clinical trials is needed. Moreover, quality benchmarks should be considered before trials are included in a meta-analysis, or replications are attempted.

The evidence base for psychotropic drugs approved by the European Medicines Agency: a meta-assessment of all European Public Assessment Reports.

AIMS: To systematically assess the level of evidence for psychotropic drugs approved by the European Medicines Agency (EMA). METHODS: Cross-sectional analysis of all European Public Assessment Reports (EPARs) and meta-analyses of the many studies reported in these EPARs. Eligible EPARs were identified from the EMA's website and individual study reports were requested from the Agency when necessary. All marketing authorisation applications (defined by the drug, the route of administration and given indications) for psychotropic medications for adults (including drugs used in psychiatry and addictology) were considered. EPARs solely based on bioequivalence studies were excluded. Our primary outcome measure was the presence of robust evidence of comparative effectiveness, defined as at least two 'positive' superiority studies against an active comparator. Various other features of the approvals were assessed, such as evidence of non-inferiority v. active comparator and superiority v. placebo. For studies with available data, effect sizes were computed and pooled using a random effect meta-analysis for each dose of each drug in each indication. RESULTS: Twenty-seven marketing authorisations were identified. For one, comparative effectiveness was explicitly considered as not needed in the EPAR. Of those remaining, 21/26 (81%) did not provide any evidence of superiority against an active comparator, 2/26 (8%) were based on at least two trials showing superiority against active comparator and three (11%) were based on one positive trial; 1/26 provided evidence for two positive non-inferiority analyses v. active comparator and seven (26%) provided evidence for one. In total, 20/27 (74%) evaluations reported evidence of superiority v. placebo with two or more trials. Among the meta-analyses of initiation studies against active comparator (57 available comparisons), the median effect size was 0.051 (range -0.503; 0.318). Twenty approved evaluations (74%) reported evidence of superiority v. placebo on the basis of two or more initiation trials and seven based on a single trial. Among meta-analyses of initiation studies against placebo (125 available comparisons), the median effect size was -0.283 (range -0.820; 0.091). Importantly, among the 89 study reports requested on the EMA website, only 19 were made available 1 year after our requests. CONCLUSIONS: The evidence for psychiatric drug approved by the EMA was in general poor. Small to modest effects v. placebo were considered sufficient in indications where an earlier drug exists. Data retrieval was incomplete after 1 year despite EMA's commitment to transparency. Improvements are needed.